Computer systems these days can employ a variety of relatively inexpensive, and often unobtrusive, sensors that measure users' physiological and/or behavioral signals. For example, cameras and microphones are able to track user's gestures, facial expressions, and voice. In addition, there are various types of physiological sensors that can measure physiological signals such as heart rate, blood-volume pulse, galvanic skin response (GSR), skin temperature, respiration, or brainwave activity. These sensors come in many forms, and can be attached to, or embedded in, devices, clothing, and even implanted in the human body. Since different sensors can capture different signals, it is not uncommon for a user to be simultaneously measured with a plurality of sensors of different types. Information collected by one or more such sensors paves the way to affective computing applications, in which computer systems can deduce how users feel and gain insight towards the emotional responses the users express.
The knowledge about a user's emotional response may be utilized by the affective computing applications in order to deliver content that suits better the user's desire and taste. Through numerous interactions with computer systems in their day-to-day lives, users are exposed to a wide array of content (e.g., communications, video clips, games). To gain input on how users feel towards the content they present, throughout these many interactions, affective computing systems may request that users' affective response be measured to certain segments of the content. For example, a computer game may indicate that it would like the user's affective response to be measured when special events occur, e.g., a new character is introduced to a scene, in order to learn the user's affective response to the event. Similarly, a virtual agent may request to have the user's affective response measured when the user views certain segments of video selected by the agent for the user. Requests and/or instructions from affective computing applications to measure a user's affective response may include details pertaining to the duration of the desired measurement and/or the type of sensors and/or settings that should be used.
The sensors used to measure users are typically mobile and/or wireless, e.g., bracelets with GSR sensors, cameras, headsets with electroencephalography (EEG) sensors, or implants; thus, they often rely on batteries for power. However, taking the multitude of measurements of a user's affective response to content, to satisfy requests made by affective computing application, may consume a lot of power that may drain the sensors' power source. Thus, enabling computer systems to continually receive user's measurements of affective response can be problematic when battery powered sensors are concerned.
The need to supply sufficient power for operating the sensors and/or analyzing the data they produce is currently met by unsatisfactory means. For example, the size of the battery powering a sensor may be increased, but this is undesirable since it makes the mobile devices more cumbersome and less comfortable for the user. Another inconvenient possibility is to recharge power sources more often to meet the power demands. Yet another possibility is to measure users less or decrease the quality of the measurements, thus reducing the sensors' power demands. But this is also problematic, since it means that the computer systems may reduce the quality of service provided to the users.
Thus, there is a need to reduce the power consumption of sensors used to fulfill requests to measure affective response, and to do so without dramatically reducing the utility of data provided by the sensors.